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Technology in the Global Perspective

Assignment -1

Ravi kumar Meduri

1.)Describe the key IS management issues in developing countries and the

underlying causes for these issues. What other issues do you see emerging in the

advanced countries in the next few years?

Ans) There have been periodical studies on key IS management issues facing the IT

industry in North America; however, an empirical investigation on key IS management

issues in developing countries has been largely ad hoc and inadequate. This identifies and

analyzes important issues faced by CIOs in the developing country of China. The results of

this study are based on two national wide CIO surveys in China, where the first was

conducted in 2004 and followed by a more recent survey in 2008. The authors provide

insight for both IS practitioners and researchers who have interests in developing countries.

Data analysis indentified key IS management issues and demonstrated similarities as well

as differences between the two rounds of surveys. Although some strategic IS issues were

still within the top 10 on both the 2004 and 2008 lists, their importance ratings weredifferent. Implications of the findings are also discussed. the key concerns of IS executives

in these areas, focusing on identifying and explaining regional similarities and differences.

Internationally, there are substantial differences in key issues. Possible reasons for these

differences--cultural, economic development, political/legal environment, and technological

status--are discussed. The analysis suggests that national culture and economic

development can explain differences in key issues.

In the context of competition between local and multinational corporations as well as how

the diffusive interactions between technologies affect their dominance in electronic markets.

Drawing on existing theories of innovation diffusion, and competitive dynamics, the authors

adopted a new diffusion model that incorporates the influence of one technologys adoptionon the diffusion of other technology. The authors then validated the model using

longitudinal field data of the two pairs of Internet technology products in Chinese electronic

markets. The findings of this investigation suggest that Internet product diffusion can be

better predicted by a competitive dynamic model than by an independent-diffusion-process

model. Further, results indicate that the diffusive interaction between local and

multinational corporations technologies can be a two-way asymmetric interaction. Such a

pattern supports a conclusion of significant second-mover advantage for local online

vendors in fast-growing emerging markets. The authors also examine the policy implications

of these results, specifically with respect to how asymmetric interaction effects can help

domestic online vendors gain second-mover advantage facing the entry of multinational

corporations.

As multinational firms increasingly adopt collaborative technology with supply chain partners

in other countries, their implementation strategies need to accommodate cultural

differences. This paper draws upon Hofstedes framework for understanding national cultural

characteristics to propose differences in implementation timing and strategy. These

propositions are tested with a case study involving a large U.S. based multinationals

implementation of Collaborative Planning, Forecasting and Replenishment (CPFR) with

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partners in four different countries: U.S., Germany, China, and Poland. This research

suggests that cultural differences impact the rollout life cycle for CPFR. The authors

recommend that (1) implementation strategies should match national culture, (2)

implementation timing can be a function of national culture, and finally (3) customer

readiness assessments for CPFR rollout should include an assessment of national cultural

differences. Decision making at the national level in both developing and developed

countries requires the integrated use of information from a multitude of sources. Both local

and national governments in many developed countries have found geographic information

systems (GIS) to be a critical tool in resource management, regional planning, and

economic development. Unfortunately, the practical use of GIS in many developing

countries is hampered by the lack of accurate and detailed spatial and demographic data,

political considerations, and management issues. To highlight importance of these issues,

we present a framework for GIS adoption in less developed countries and discuss these and

other constraints in the context of this framework. We also offer ideas for technical,

managerial, and policy initiatives that should be helpful in addressing impediments to GIS

adoption. These ideas are summarized in a set of propositions and a related framework that

shows our expectations about the impact of these initiatives on implementation success

Information sharing has recently received considerable academic interest because of the

importance knowledge management plays in the creation of sustained competitive

advantage for global firms. The interest is attributed to the need for achieving higher levels

of worker empowerment and effectiveness. However, the existing research in the area lacks

an examination of how national differences impact information sharing activities. This study

responds to this need by presenting a structured yet exploratory inquiry into factors

impacting information sharing and the adoption of Human Resource Information Systems

(HRIS) by examining key national differences. Assessing national differences is extended

beyond the examination of national culture by including institutional contexts in the study.

Using a 22-country sample from the CRANET database, the study suggests there is a

significant and predictable variation in the level of information sharing and HRIS adoption in

firms from different countries, and that national differences, including cultural and

institutional contexts, have an impact on information sharing. The study also indicates that

the level of HRIS adoption is positively associated with information sharing. The authors

discuss these findings, their implications for research and practice, and address limitations

along with opportunities for future research. Individuals have to disclose personal

information in order to utilize the manifold options of the Internet. Online users frequently

trade data for benefits (privacy calculus). Trust in both the Internet and the vendor has

been identified as an important antecedent to disclosing personal information online. The

authors introduce the perceived risk of disclosing specific data types as an additional factor

in the field of study. The results from a survey in three countries (Austria, Australia, and

Hong Kong) show that the perceived risk of disclosing personal information is a stronger

stimulus for the intention to provide personal information than having trust in the Internet

or in the online vendor. Several significant differences are found in the relationships

between the perceived risk of disclosing personal information, trust, and the willingness to

disclose personal information.

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. Focus on case studies: The literature on IS in DCs has grown, but it is a literature

dominated by case studies of individual IS projects. Taken alone, these provide no basis for

estimation of overall failure/success rates. Despite these limitations, there are some

glimpses of evidence.

A few more specific multiple-case studies have been conducted, with examples summarized

here:-

. Health information systems in South Africa: Braa and Hedberg (2002) reported widespread

partial failure of high cost systems with little use of data.

. IS in the Thai public sector: Kitiyadisai (2000) reported failure cases seem to be the norm

in Thailand at all governmental levels.

. Donor-funded IT projects in China: Baark and Heeks (1999) reported that all were found

to be partial failures.

. World Bank-funded IT projects in Africa: Moussa and Schware (1992) reported almost all

as partialoften sustainabilityfailures.

In summary, the evidence base is not strongand it urgently needs strengtheningbut it

all points in one direction: toward high rates of IS failure in developing countries.

Information systems in Knowledge Management:-

Knowledge management is more a methodology applied to business practices than a

technology or product. Nevertheless, information technology is crucial to the success of

every knowledge management system. Information technology enables KM by providing the

enterprise architecture upon which it is built.

Knowledge management systems are developed using three sets of technologies:

communication, collaboration, and storage and retrieval. Communication technologies allowusers to access needed knowledge, and to communicate with each otherespecially with

experts. E-mail, the Internet, corporate intranets, and other Web-based tools provide

communication capabilities. Even fax machines and the telephone are used for

communication, especially when the practice approach to knowledge management is

adopted. Collaboration technologies provide the means to perform group work. Groups can

work together on common documents at the same time (synchronous) or at different times

(asynchronous); in the same place, or in different places. This is especially important for

members of a community of practice working on knowledge contributions. Collaborative

computing capabilities such as electronic brainstorming enhance group work, especially for

knowledge contribution. Additional forms of group work involve experts working with

individuals try-in to apply their knowledge. This requires collaboration at a fairly high level.Other collaborative computing systems allow an organization to create a virtual space so

that individuals can work online anywhere and at any time.

Storage and retrieval technologies originally meant using a database management system

to store and manage knowledge. This worked reasonably well in the early days for storing

and managing most explicit knowledge, and even explicit knowledge about tacit knowledge.

However, capturing, storing, and managing tacit knowledge usually requires a different set

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of tools. Electronic document management systems and specialized storage systems that

are part of collaborative computing systems fill this void

Technologies Supporting the management system:-

Several technologies have contributed to significant advances in knowledge management

tools. Artificial intelligence, intelligent agents, knowledge discovery in databases, and

Extensible Markup Language (XML) are examples of technologies that enableadvanced functionality of modern knowledge management systems and form the base for

future innovations in the KM field.

ARTIFICIAL INTELLIGENCE :-

In the definition of knowledge management, artificial intelligence is rarely mentioned.

However, practically speaking, AI methods and tools are embedded in a number of

knowledge management systems, either by vendors or by system developers.

AI methods can assist in identifying expertise, eliciting knowledge automatically and semi

automatically, interfacing through natural language processing, and in intelligent search

through intelligent agents. AI methods, notably expert systems, neural networks, fuzzy logic,and intelligent agents, are used in knowledge management systems to perform various

functions: They assist in and enhance searching knowledge (e.g., intelligent agents in Web

searches), including scanning e-mail, documents, and databases and helping establish

knowledge profiles of individuals and groups. They forecast future results using existing

knowledge. AI methods help determine the relative importance of knowledge, when

knowledge is both contributed to and accessed from the knowledge repository, and help

determine meaningful relationships in the knowledge. They identify patterns in data (usually

through neural networks), induce rules for expert systems, and provide advice directly from

knowledge by using neural networks or expert systems. Finally, they provide a natural

language or voice commanddriven user interface for a knowledge management system.

INTELLIGENT AGENTS:-

Intelligent agents are software systems that learn how users work and provide assistance in

their daily tasks. For example, when these software programs are e told what the user

wants to retrieve, passive agents can monitor incoming information for matches with user

interests and active agents can seek out information relevant to user preferences (Gray and

Tehran, 2003).

There are a number of ways that intelligent agents can help in knowledge management

systems. Typically they are used to elicit and identify knowledge. Examples are:

IBM (ibm.com) offers an intelligent data mining family, including Intelligent DecisionServer (IDS), for finding and analyzing massive amounts of enterprise data.

Gentia (Planning Sciences International,gentia.com) uses intelligent agents to facilitate

data mining with Web access and data warehouse facilities.

Convectis (HNC Software Inc.) uses neural networks to search text data and

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